Technologies for enabling and metering the utilization of features on demand

ABSTRACT

Technologies for enabling and metering the utilization of components on demand include a compute device. The compute device includes a network interface controller and circuitry configured to receive, through a network and with the network interface controller, a request to enable a component of a sled to assist in the execution of a workload. The circuitry is further configured to enable, in response to the request, the component to assist in the execution of the workload, and meter the utilization of the component by the workload to determine a total monetary cost to a customer associated with the workload for the utilization of the component.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of Indian Provisional Patent Application No. 201741030632, filed Aug. 30, 2017 and U.S. Provisional Patent Application No. 62/584,401, filed Nov. 10, 2017.

BACKGROUND

In some data centers, such as cloud data centers, compute devices execute operations (e.g., workloads executed in virtualized environments, such as in virtual machines or in containers) on behalf of customers as a service. A data center may be provisioned with a variety of hardware components that, for the data center operator, may have different levels of monetary expenditures associated with them (e.g., initial purchase cost, electricity costs for powering the components even when they are not being used by a service, electricity costs for cooling the components, etc.) that are not directly accounted for and passed on to the customers who are using the services of the data center. Rather, the agreement between the data center operator, known as service level agreement (SLA), typically specifies a set of quality of service (QoS) targets (e.g., a target latency, a target throughput, etc.) that the data center is expected to satisfy and a fee (e.g., a monthly fee) for satisfying the QoS targets, without regard to the specific hardware components utilized to satisfy the QoS targets.

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and not by way of limitation in the accompanying figures. For simplicity and clarity of illustration, elements illustrated in the figures are not necessarily drawn to scale. Where considered appropriate, reference labels have been repeated among the figures to indicate corresponding or analogous elements.

FIG. 1 is a simplified diagram of at least one embodiment of a data center for executing workloads with disaggregated resources;

FIG. 2 is a simplified diagram of at least one embodiment of a pod that may be included in the data center of FIG. 1;

FIG. 3 is a perspective view of at least one embodiment of a rack that may be included in the pod of FIG. 2;

FIG. 4 is a side elevation view of the rack of FIG. 3;

FIG. 5 is a perspective view of the rack of FIG. 3 having a sled mounted therein;

FIG. 6 is a is a simplified block diagram of at least one embodiment of a top side of the sled of FIG. 5;

FIG. 7 is a simplified block diagram of at least one embodiment of a bottom side of the sled of FIG. 6;

FIG. 8 is a simplified block diagram of at least one embodiment of a compute sled usable in the data center of FIG. 1;

FIG. 9 is a top perspective view of at least one embodiment of the compute sled of FIG. 8;

FIG. 10 is a simplified block diagram of at least one embodiment of an accelerator sled usable in the data center of FIG. 1;

FIG. 11 is a top perspective view of at least one embodiment of the accelerator sled of FIG. 10;

FIG. 12 is a simplified block diagram of at least one embodiment of a storage sled usable in the data center of FIG. 1;

FIG. 13 is a top perspective view of at least one embodiment of the storage sled of FIG. 12;

FIG. 14 is a simplified block diagram of at least one embodiment of a memory sled usable in the data center of FIG. 1; and

FIG. 15 is a simplified block diagram of a system that may be established within the data center of FIG. 1 to execute workloads with managed nodes composed of disaggregated resources;

FIG. 16 is a simplified block diagram of at least one embodiment of a system for enabling and metering the utilization of features of components on an as-requested basis in a disaggregated architecture; and

FIGS. 17-19 are a simplified block diagram of at least one embodiment of a method for enabling and metering the utilization of features of components on an as-requested basis that may be performed by an orchestrator server in the system of FIG. 16.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will be described herein in detail. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,” “an illustrative embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include that particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. Additionally, it should be appreciated that items included in a list in the form of “at least one A, B, and C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C). Similarly, items listed in the form of “at least one of A, B, or C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

The disclosed embodiments may be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. A machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown in specific arrangements and/or orderings. However, it should be appreciated that such specific arrangements and/or orderings may not be required. Rather, in some embodiments, such features may be arranged in a different manner and/or order than shown in the illustrative figures. Additionally, the inclusion of a structural or method feature in a particular figure is not meant to imply that such feature is required in all embodiments and, in some embodiments, may not be included or may be combined with other features.

Referring now to FIG. 1, a data center 100 in which disaggregated resources may cooperatively execute one or more workloads (e.g., applications on behalf of customers) includes multiple pods 110, 120, 130, 140, each of which includes one or more rows of racks. Of course, although data center 100 is shown with multiple pods, in some embodiments, the data center 100 may be embodied as a single pod. As described in more detail herein, each rack houses multiple sleds, each of which may be primarily equipped with a particular type of resource (e.g., memory devices, data storage devices, accelerator devices, general purpose processors), i.e., resources that can be logically coupled to form a composed node, which can act as, for example, a server. In the illustrative embodiment, the sleds in each pod 110, 120, 130, 140 are connected to multiple pod switches (e.g., switches that route data communications to and from sleds within the pod). The pod switches, in turn, connect with spine switches 150 that switch communications among pods (e.g., the pods 110, 120, 130, 140) in the data center 100. In some embodiments, the sleds may be connected with a fabric using Intel Omni-Path technology. In other embodiments, the sleds may be connected with other fabrics, such as InfiniBand or Ethernet. As described in more detail herein, resources within sleds in the data center 100 may be allocated to a group (referred to herein as a “managed node”) containing resources from one or more sleds to be collectively utilized in the execution of a workload. The workload can execute as if the resources belonging to the managed node were located on the same sled. The resources in a managed node may belong to sleds belonging to different racks, and even to different pods 110, 120, 130, 140. As such, some resources of a single sled may be allocated to one managed node while other resources of the same sled are allocated to a different managed node (e.g., one processor assigned to one managed node and another processor of the same sled assigned to a different managed node).

A data center comprising disaggregated resources, such as data center 100, can be used in a wide variety of contexts, such as enterprise, government, cloud service provider, and communications service provider (e.g., Telco's), as well in a wide variety of sizes, from cloud service provider mega-data centers that consume over 100,000 sq. ft. to single- or multi-rack installations for use in base stations.

The disaggregation of resources to sleds comprised predominantly of a single type of resource (e.g., compute sleds comprising primarily compute resources, memory sleds containing primarily memory resources), and the selective allocation and deallocation of the disaggregated resources to form a managed node assigned to execute a workload improves the operation and resource usage of the data center 100 relative to typical data centers comprised of hyperconverged servers containing compute, memory, storage and perhaps additional resources in a single chassis. For example, because sleds predominantly contain resources of a particular type, resources of a given type can be upgraded independently of other resources. Additionally, because different resources types (processors, storage, accelerators, etc.) typically have different refresh rates, greater resource utilization and reduced total cost of ownership may be achieved. For example, a data center operator can upgrade the processors throughout their facility by only swapping out the compute sleds. In such a case, accelerator and storage resources may not be contemporaneously upgraded and, rather, may be allowed to continue operating until those resources are scheduled for their own refresh. Resource utilization may also increase. For example, if managed nodes are composed based on requirements of the workloads that will be running on them, resources within a node are more likely to be fully utilized. Such utilization may allow for more managed nodes to run in a data center with a given set of resources, or for a data center expected to run a given set of workloads, to be built using fewer resources.

Referring now to FIG. 2, the pod 110, in the illustrative embodiment, includes a set of rows 200, 210, 220, 230 of racks 240. Each rack 240 may house multiple sleds (e.g., sixteen sleds) and provide power and data connections to the housed sleds, as described in more detail herein. In the illustrative embodiment, the racks in each row 200, 210, 220, 230 are connected to multiple pod switches 250, 260. The pod switch 250 includes a set of ports 252 to which the sleds of the racks of the pod 110 are connected and another set of ports 254 that connect the pod 110 to the spine switches 150 to provide connectivity to other pods in the data center 100. Similarly, the pod switch 260 includes a set of ports 262 to which the sleds of the racks of the pod 110 are connected and a set of ports 264 that connect the pod 110 to the spine switches 150. As such, the use of the pair of switches 250, 260 provides an amount of redundancy to the pod 110. For example, if either of the switches 250, 260 fails, the sleds in the pod 110 may still maintain data communication with the remainder of the data center 100 (e.g., sleds of other pods) through the other switch 250, 260. Furthermore, in the illustrative embodiment, the switches 150, 250, 260 may be embodied as dual-mode optical switches, capable of routing both Ethernet protocol communications carrying Internet Protocol (IP) packets and communications according to a second, high-performance link-layer protocol (e.g., Intel's Omni-Path Architecture's, InfiniBand, PCI Express) via optical signaling media of an optical fabric.

It should be appreciated that each of the other pods 120, 130, 140 (as well as any additional pods of the data center 100) may be similarly structured as, and have components similar to, the pod 110 shown in and described in regard to FIG. 2 (e.g., each pod may have rows of racks housing multiple sleds as described above). Additionally, while two pod switches 250, 260 are shown, it should be understood that in other embodiments, each pod 110, 120, 130, 140 may be connected to a different number of pod switches, providing even more failover capacity. Of course, in other embodiments, pods may be arranged differently than the rows-of-racks configuration shown in FIGS. 1-2. For example, a pod may be embodied as multiple sets of racks in which each set of racks is arranged radially, i.e., the racks are equidistant from a center switch.

Referring now to FIGS. 3-5, each illustrative rack 240 of the data center 100 includes two elongated support posts 302, 304, which are arranged vertically. For example, the elongated support posts 302, 304 may extend upwardly from a floor of the data center 100 when deployed. The rack 240 also includes one or more horizontal pairs 310 of elongated support arms 312 (identified in FIG. 3 via a dashed ellipse) configured to support a sled of the data center 100 as discussed below. One elongated support arm 312 of the pair of elongated support arms 312 extends outwardly from the elongated support post 302 and the other elongated support arm 312 extends outwardly from the elongated support post 304.

In the illustrative embodiments, each sled of the data center 100 is embodied as a chassis-less sled. That is, each sled has a chassis-less circuit board substrate on which physical resources (e.g., processors, memory, accelerators, storage, etc.) are mounted as discussed in more detail below. As such, the rack 240 is configured to receive the chassis-less sleds. For example, each pair 310 of elongated support arms 312 defines a sled slot 320 of the rack 240, which is configured to receive a corresponding chassis-less sled. To do so, each illustrative elongated support arm 312 includes a circuit board guide 330 configured to receive the chassis-less circuit board substrate of the sled. Each circuit board guide 330 is secured to, or otherwise mounted to, a top side 332 of the corresponding elongated support arm 312. For example, in the illustrative embodiment, each circuit board guide 330 is mounted at a distal end of the corresponding elongated support arm 312 relative to the corresponding elongated support post 302, 304. For clarity of the Figures, not every circuit board guide 330 may be referenced in each Figure.

Each circuit board guide 330 includes an inner wall that defines a circuit board slot 380 configured to receive the chassis-less circuit board substrate of a sled 400 when the sled 400 is received in the corresponding sled slot 320 of the rack 240. To do so, as shown in FIG. 4, a user (or robot) aligns the chassis-less circuit board substrate of an illustrative chassis-less sled 400 to a sled slot 320. The user, or robot, may then slide the chassis-less circuit board substrate forward into the sled slot 320 such that each side edge 414 of the chassis-less circuit board substrate is received in a corresponding circuit board slot 380 of the circuit board guides 330 of the pair 310 of elongated support arms 312 that define the corresponding sled slot 320 as shown in FIG. 4. By having robotically accessible and robotically manipulable sleds comprising disaggregated resources, each type of resource can be upgraded independently of each other and at their own optimized refresh rate. Furthermore, the sleds are configured to blindly mate with power and data communication cables in each rack 240, enhancing their ability to be quickly removed, upgraded, reinstalled, and/or replaced. As such, in some embodiments, the data center 100 may operate (e.g., execute workloads, undergo maintenance and/or upgrades, etc.) without human involvement on the data center floor. In other embodiments, a human may facilitate one or more maintenance or upgrade operations in the data center 100.

It should be appreciated that each circuit board guide 330 is dual sided. That is, each circuit board guide 330 includes an inner wall that defines a circuit board slot 380 on each side of the circuit board guide 330. In this way, each circuit board guide 330 can support a chassis-less circuit board substrate on either side. As such, a single additional elongated support post may be added to the rack 240 to turn the rack 240 into a two-rack solution that can hold twice as many sled slots 320 as shown in FIG. 3. The illustrative rack 240 includes seven pairs 310 of elongated support arms 312 that define a corresponding seven sled slots 320, each configured to receive and support a corresponding sled 400 as discussed above. Of course, in other embodiments, the rack 240 may include additional or fewer pairs 310 of elongated support arms 312 (i.e., additional or fewer sled slots 320). It should be appreciated that because the sled 400 is chassis-less, the sled 400 may have an overall height that is different than typical servers. As such, in some embodiments, the height of each sled slot 320 may be shorter than the height of a typical server (e.g., shorter than a single rank unit, “1U”). That is, the vertical distance between each pair 310 of elongated support arms 312 may be less than a standard rack unit “1U.” Additionally, due to the relative decrease in height of the sled slots 320, the overall height of the rack 240 in some embodiments may be shorter than the height of traditional rack enclosures. For example, in some embodiments, each of the elongated support posts 302, 304 may have a length of six feet or less. Again, in other embodiments, the rack 240 may have different dimensions. For example, in some embodiments, the vertical distance between each pair 310 of elongated support arms 312 may be greater than a standard rack until “1U”. In such embodiments, the increased vertical distance between the sleds allows for larger heat sinks to be attached to the physical resources and for larger fans to be used (e.g., in the fan array 370 described below) for cooling each sled, which in turn can allow the physical resources to operate at increased power levels. Further, it should be appreciated that the rack 240 does not include any walls, enclosures, or the like. Rather, the rack 240 is an enclosure-less rack that is opened to the local environment. Of course, in some cases, an end plate may be attached to one of the elongated support posts 302, 304 in those situations in which the rack 240 forms an end-of-row rack in the data center 100.

In some embodiments, various interconnects may be routed upwardly or downwardly through the elongated support posts 302, 304. To facilitate such routing, each elongated support post 302, 304 includes an inner wall that defines an inner chamber in which interconnects may be located. The interconnects routed through the elongated support posts 302, 304 may be embodied as any type of interconnects including, but not limited to, data or communication interconnects to provide communication connections to each sled slot 320, power interconnects to provide power to each sled slot 320, and/or other types of interconnects.

The rack 240, in the illustrative embodiment, includes a support platform on which a corresponding optical data connector (not shown) is mounted. Each optical data connector is associated with a corresponding sled slot 320 and is configured to mate with an optical data connector of a corresponding sled 400 when the sled 400 is received in the corresponding sled slot 320. In some embodiments, optical connections between components (e.g., sleds, racks, and switches) in the data center 100 are made with a blind mate optical connection. For example, a door on each cable may prevent dust from contaminating the fiber inside the cable. In the process of connecting to a blind mate optical connector mechanism, the door is pushed open when the end of the cable approaches or enters the connector mechanism. Subsequently, the optical fiber inside the cable may enter a gel within the connector mechanism and the optical fiber of one cable comes into contact with the optical fiber of another cable within the gel inside the connector mechanism.

The illustrative rack 240 also includes a fan array 370 coupled to the cross-support arms of the rack 240. The fan array 370 includes one or more rows of cooling fans 372, which are aligned in a horizontal line between the elongated support posts 302, 304. In the illustrative embodiment, the fan array 370 includes a row of cooling fans 372 for each sled slot 320 of the rack 240. As discussed above, each sled 400 does not include any on-board cooling system in the illustrative embodiment and, as such, the fan array 370 provides cooling for each sled 400 received in the rack 240. Each rack 240, in the illustrative embodiment, also includes a power supply associated with each sled slot 320. Each power supply is secured to one of the elongated support arms 312 of the pair 310 of elongated support arms 312 that define the corresponding sled slot 320. For example, the rack 240 may include a power supply coupled or secured to each elongated support arm 312 extending from the elongated support post 302. Each power supply includes a power connector configured to mate with a power connector of the sled 400 when the sled 400 is received in the corresponding sled slot 320. In the illustrative embodiment, the sled 400 does not include any on-board power supply and, as such, the power supplies provided in the rack 240 supply power to corresponding sleds 400 when mounted to the rack 240. Each power supply is configured to satisfy the power requirements for its associated sled, which can vary from sled to sled. Additionally, the power supplies provided in the rack 240 can operate independent of each other. That is, within a single rack, a first power supply providing power to a compute sled can provide power levels that are different than power levels supplied by a second power supply providing power to an accelerator sled. The power supplies may be controllable at the sled level or rack level, and may be controlled locally by components on the associated sled or remotely, such as by another sled or an orchestrator.

Referring now to FIG. 6, the sled 400, in the illustrative embodiment, is configured to be mounted in a corresponding rack 240 of the data center 100 as discussed above. In some embodiments, each sled 400 may be optimized or otherwise configured for performing particular tasks, such as compute tasks, acceleration tasks, data storage tasks, etc. For example, the sled 400 may be embodied as a compute sled 800 as discussed below in regard to FIGS. 8-9, an accelerator sled 1000 as discussed below in regard to FIGS. 10-11, a storage sled 1200 as discussed below in regard to FIGS. 12-13, or as a sled optimized or otherwise configured to perform other specialized tasks, such as a memory sled 1400, discussed below in regard to FIG. 14.

As discussed above, the illustrative sled 400 includes a chassis-less circuit board substrate 602, which supports various physical resources (e.g., electrical components) mounted thereon. It should be appreciated that the circuit board substrate 602 is “chassis-less” in that the sled 400 does not include a housing or enclosure. Rather, the chassis-less circuit board substrate 602 is open to the local environment. The chassis-less circuit board substrate 602 may be formed from any material capable of supporting the various electrical components mounted thereon. For example, in an illustrative embodiment, the chassis-less circuit board substrate 602 is formed from an FR-4 glass-reinforced epoxy laminate material. Of course, other materials may be used to form the chassis-less circuit board substrate 602 in other embodiments.

As discussed in more detail below, the chassis-less circuit board substrate 602 includes multiple features that improve the thermal cooling characteristics of the various electrical components mounted on the chassis-less circuit board substrate 602. As discussed, the chassis-less circuit board substrate 602 does not include a housing or enclosure, which may improve the airflow over the electrical components of the sled 400 by reducing those structures that may inhibit air flow. For example, because the chassis-less circuit board substrate 602 is not positioned in an individual housing or enclosure, there is no vertically-arranged backplane (e.g., a backplate of the chassis) attached to the chassis-less circuit board substrate 602, which could inhibit air flow across the electrical components. Additionally, the chassis-less circuit board substrate 602 has a geometric shape configured to reduce the length of the airflow path across the electrical components mounted to the chassis-less circuit board substrate 602. For example, the illustrative chassis-less circuit board substrate 602 has a width 604 that is greater than a depth 606 of the chassis-less circuit board substrate 602. In one particular embodiment, for example, the chassis-less circuit board substrate 602 has a width of about 21 inches and a depth of about 9 inches, compared to a typical server that has a width of about 17 inches and a depth of about 39 inches. As such, an airflow path 608 that extends from a front edge 610 of the chassis-less circuit board substrate 602 toward a rear edge 612 has a shorter distance relative to typical servers, which may improve the thermal cooling characteristics of the sled 400. Furthermore, although not illustrated in FIG. 6, the various physical resources mounted to the chassis-less circuit board substrate 602 are mounted in corresponding locations such that no two substantively heat-producing electrical components shadow each other as discussed in more detail below. That is, no two electrical components, which produce appreciable heat during operation (i.e., greater than a nominal heat sufficient enough to adversely impact the cooling of another electrical component), are mounted to the chassis-less circuit board substrate 602 linearly in-line with each other along the direction of the airflow path 608 (i.e., along a direction extending from the front edge 610 toward the rear edge 612 of the chassis-less circuit board substrate 602).

As discussed above, the illustrative sled 400 includes one or more physical resources 620 mounted to a top side 650 of the chassis-less circuit board substrate 602. Although two physical resources 620 are shown in FIG. 6, it should be appreciated that the sled 400 may include one, two, or more physical resources 620 in other embodiments. The physical resources 620 may be embodied as any type of processor, controller, or other compute circuit capable of performing various tasks such as compute functions and/or controlling the functions of the sled 400 depending on, for example, the type or intended functionality of the sled 400. For example, as discussed in more detail below, the physical resources 620 may be embodied as high-performance processors in embodiments in which the sled 400 is embodied as a compute sled, as accelerator co-processors or circuits in embodiments in which the sled 400 is embodied as an accelerator sled, storage controllers in embodiments in which the sled 400 is embodied as a storage sled, or a set of memory devices in embodiments in which the sled 400 is embodied as a memory sled.

The sled 400 also includes one or more additional physical resources 630 mounted to the top side 650 of the chassis-less circuit board substrate 602. In the illustrative embodiment, the additional physical resources include a network interface controller (NIC) as discussed in more detail below. Of course, depending on the type and functionality of the sled 400, the physical resources 630 may include additional or other electrical components, circuits, and/or devices in other embodiments.

The physical resources 620 are communicatively coupled to the physical resources 630 via an input/output (I/O) subsystem 622. The I/O subsystem 622 may be embodied as circuitry and/or components to facilitate input/output operations with the physical resources 620, the physical resources 630, and/or other components of the sled 400. For example, the I/O subsystem 622 may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, integrated sensor hubs, firmware devices, communication links (e.g., point-to-point links, bus links, wires, cables, waveguides, light guides, printed circuit board traces, etc.), and/or other components and subsystems to facilitate the input/output operations. In the illustrative embodiment, the I/O subsystem 622 is embodied as, or otherwise includes, a double data rate 4 (DDR4) data bus or a DDRS data bus.

In some embodiments, the sled 400 may also include a resource-to-resource interconnect 624. The resource-to-resource interconnect 624 may be embodied as any type of communication interconnect capable of facilitating resource-to-resource communications. In the illustrative embodiment, the resource-to-resource interconnect 624 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the resource-to-resource interconnect 624 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to resource-to-resource communications.

The sled 400 also includes a power connector 640 configured to mate with a corresponding power connector of the rack 240 when the sled 400 is mounted in the corresponding rack 240. The sled 400 receives power from a power supply of the rack 240 via the power connector 640 to supply power to the various electrical components of the sled 400. That is, the sled 400 does not include any local power supply (i.e., an on-board power supply) to provide power to the electrical components of the sled 400. The exclusion of a local or on-board power supply facilitates the reduction in the overall footprint of the chassis-less circuit board substrate 602, which may increase the thermal cooling characteristics of the various electrical components mounted on the chassis-less circuit board substrate 602 as discussed above. In some embodiments, voltage regulators are placed on a bottom side 750 (see FIG. 7) of the chassis-less circuit board substrate 602 directly opposite of the processors 820 (see FIG. 8), and power is routed from the voltage regulators to the processors 820 by vias extending through the circuit board substrate 602. Such a configuration provides an increased thermal budget, additional current and/or voltage, and better voltage control relative to typical printed circuit boards in which processor power is delivered from a voltage regulator, in part, by printed circuit traces.

In some embodiments, the sled 400 may also include mounting features 642 configured to mate with a mounting arm, or other structure, of a robot to facilitate the placement of the sled 600 in a rack 240 by the robot. The mounting features 642 may be embodied as any type of physical structures that allow the robot to grasp the sled 400 without damaging the chassis-less circuit board substrate 602 or the electrical components mounted thereto. For example, in some embodiments, the mounting features 642 may be embodied as non-conductive pads attached to the chassis-less circuit board substrate 602. In other embodiments, the mounting features may be embodied as brackets, braces, or other similar structures attached to the chassis-less circuit board substrate 602. The particular number, shape, size, and/or make-up of the mounting feature 642 may depend on the design of the robot configured to manage the sled 400.

Referring now to FIG. 7, in addition to the physical resources 630 mounted on the top side 650 of the chassis-less circuit board substrate 602, the sled 400 also includes one or more memory devices 720 mounted to a bottom side 750 of the chassis-less circuit board substrate 602. That is, the chassis-less circuit board substrate 602 is embodied as a double-sided circuit board. The physical resources 620 are communicatively coupled to the memory devices 720 via the I/O subsystem 622. For example, the physical resources 620 and the memory devices 720 may be communicatively coupled by one or more vias extending through the chassis-less circuit board substrate 602. Each physical resource 620 may be communicatively coupled to a different set of one or more memory devices 720 in some embodiments. Alternatively, in other embodiments, each physical resource 620 may be communicatively coupled to each memory device 720.

The memory devices 720 may be embodied as any type of memory device capable of storing data for the physical resources 620 during operation of the sled 400, such as any type of volatile (e.g., dynamic random access memory (DRAM), etc.) or non-volatile memory. Volatile memory may be a storage medium that requires power to maintain the state of data stored by the medium. Non-limiting examples of volatile memory may include various types of random access memory (RAM), such as dynamic random access memory (DRAM) or static random access memory (SRAM). One particular type of DRAM that may be used in a memory module is synchronous dynamic random access memory (SDRAM). In particular embodiments, DRAM of a memory component may comply with a standard promulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2 SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 for Low Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, and JESD209-4 for LPDDR4. Such standards (and similar standards) may be referred to as DDR-based standards and communication interfaces of the storage devices that implement such standards may be referred to as DDR-based interfaces.

In one embodiment, the memory device 720 is a block addressable memory device, such as those based on NAND or NOR technologies. A memory device may also include next-generation nonvolatile devices, such as Intel 3D XPoint™ memory or other byte addressable write-in-place nonvolatile memory devices. In one embodiment, the memory device may be or may include memory devices that use chalcogenide glass, multi-threshold level NAND flash memory, NOR flash memory, single or multi-level Phase Change Memory (PCM), a resistive memory, nanowire memory, ferroelectric transistor random access memory (FeTRAM), anti-ferroelectric memory, magnetoresistive random access memory (MRAM) memory that incorporates memristor technology, resistive memory including the metal oxide base, the oxygen vacancy base and the conductive bridge Random Access Memory (CB-RAM), or spin transfer torque (STT)-MRAM, a spintronic magnetic junction memory based device, a magnetic tunneling junction (MTJ) based device, a DW (Domain Wall) and SOT (Spin Orbit Transfer) based device, a thyristor based memory device, or a combination of any of the above, or other memory. The memory device may refer to the die itself and/or to a packaged memory product. In some embodiments, the memory device may comprise a transistor-less stackable cross point architecture in which memory cells sit at the intersection of word lines and bit lines and are individually addressable and in which bit storage is based on a change in bulk resistance.

Referring now to FIG. 8, in some embodiments, the sled 400 may be embodied as a compute sled 800. The compute sled 800 is optimized, or otherwise configured, to perform compute tasks. Of course, as discussed above, the compute sled 800 may rely on other sleds, such as acceleration sleds and/or storage sleds, to perform such compute tasks. The compute sled 800 includes various physical resources (e.g., electrical components) similar to the physical resources of the sled 400, which have been identified in FIG. 8 using the same reference numbers. The description of such components provided above in regard to FIGS. 6 and 7 applies to the corresponding components of the compute sled 800 and is not repeated herein for clarity of the description of the compute sled 800.

In the illustrative compute sled 800, the physical resources 620 are embodied as processors 820. Although only two processors 820 are shown in FIG. 8, it should be appreciated that the compute sled 800 may include additional processors 820 in other embodiments. Illustratively, the processors 820 are embodied as high-performance processors 820 and may be configured to operate at a relatively high power rating. Although the processors 820 generate additional heat operating at power ratings greater than typical processors (which operate at around 155-230 W), the enhanced thermal cooling characteristics of the chassis-less circuit board substrate 602 discussed above facilitate the higher power operation. For example, in the illustrative embodiment, the processors 820 are configured to operate at a power rating of at least 250 W. In some embodiments, the processors 820 may be configured to operate at a power rating of at least 350 W.

In some embodiments, the compute sled 800 may also include a processor-to-processor interconnect 842. Similar to the resource-to-resource interconnect 624 of the sled 400 discussed above, the processor-to-processor interconnect 842 may be embodied as any type of communication interconnect capable of facilitating processor-to-processor interconnect 842 communications. In the illustrative embodiment, the processor-to-processor interconnect 842 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the processor-to-processor interconnect 842 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications.

The compute sled 800 also includes a communication circuit 830. The illustrative communication circuit 830 includes a network interface controller (NIC) 832, which may also be referred to as a host fabric interface (HFI). The NIC 832 may be embodied as, or otherwise include, any type of integrated circuit, discrete circuits, controller chips, chipsets, add-in-boards, daughtercards, network interface cards, or other devices that may be used by the compute sled 800 to connect with another compute device (e.g., with other sleds 400). In some embodiments, the NIC 832 may be embodied as part of a system-on-a-chip (SoC) that includes one or more processors, or included on a multichip package that also contains one or more processors. In some embodiments, the NIC 832 may include a local processor (not shown) and/or a local memory (not shown) that are both local to the NIC 832. In such embodiments, the local processor of the NIC 832 may be capable of performing one or more of the functions of the processors 820. Additionally or alternatively, in such embodiments, the local memory of the NIC 832 may be integrated into one or more components of the compute sled at the board level, socket level, chip level, and/or other levels.

The communication circuit 830 is communicatively coupled to an optical data connector 834. The optical data connector 834 is configured to mate with a corresponding optical data connector of the rack 240 when the compute sled 800 is mounted in the rack 240. Illustratively, the optical data connector 834 includes a plurality of optical fibers which lead from a mating surface of the optical data connector 834 to an optical transceiver 836. The optical transceiver 836 is configured to convert incoming optical signals from the rack-side optical data connector to electrical signals and to convert electrical signals to outgoing optical signals to the rack-side optical data connector. Although shown as forming part of the optical data connector 834 in the illustrative embodiment, the optical transceiver 836 may form a portion of the communication circuit 830 in other embodiments.

In some embodiments, the compute sled 800 may also include an expansion connector 840. In such embodiments, the expansion connector 840 is configured to mate with a corresponding connector of an expansion chassis-less circuit board substrate to provide additional physical resources to the compute sled 800. The additional physical resources may be used, for example, by the processors 820 during operation of the compute sled 800. The expansion chassis-less circuit board substrate may be substantially similar to the chassis-less circuit board substrate 602 discussed above and may include various electrical components mounted thereto. The particular electrical components mounted to the expansion chassis-less circuit board substrate may depend on the intended functionality of the expansion chassis-less circuit board substrate. For example, the expansion chassis-less circuit board substrate may provide additional compute resources, memory resources, and/or storage resources. As such, the additional physical resources of the expansion chassis-less circuit board substrate may include, but is not limited to, processors, memory devices, storage devices, and/or accelerator circuits including, for example, field programmable gate arrays (FPGA), application-specific integrated circuits (ASICs), security co-processors, graphics processing units (GPUs), machine learning circuits, or other specialized processors, controllers, devices, and/or circuits.

Referring now to FIG. 9, an illustrative embodiment of the compute sled 800 is shown. As shown, the processors 820, communication circuit 830, and optical data connector 834 are mounted to the top side 650 of the chassis-less circuit board substrate 602. Any suitable attachment or mounting technology may be used to mount the physical resources of the compute sled 800 to the chassis-less circuit board substrate 602. For example, the various physical resources may be mounted in corresponding sockets (e.g., a processor socket), holders, or brackets. In some cases, some of the electrical components may be directly mounted to the chassis-less circuit board substrate 602 via soldering or similar techniques.

As discussed above, the individual processors 820 and communication circuit 830 are mounted to the top side 650 of the chassis-less circuit board substrate 602 such that no two heat-producing, electrical components shadow each other. In the illustrative embodiment, the processors 820 and communication circuit 830 are mounted in corresponding locations on the top side 650 of the chassis-less circuit board substrate 602 such that no two of those physical resources are linearly in-line with others along the direction of the airflow path 608. It should be appreciated that, although the optical data connector 834 is in-line with the communication circuit 830, the optical data connector 834 produces no or nominal heat during operation.

The memory devices 720 of the compute sled 800 are mounted to the bottom side 750 of the of the chassis-less circuit board substrate 602 as discussed above in regard to the sled 400. Although mounted to the bottom side 750, the memory devices 720 are communicatively coupled to the processors 820 located on the top side 650 via the I/O subsystem 622. Because the chassis-less circuit board substrate 602 is embodied as a double-sided circuit board, the memory devices 720 and the processors 820 may be communicatively coupled by one or more vias, connectors, or other mechanisms extending through the chassis-less circuit board substrate 602. Of course, each processor 820 may be communicatively coupled to a different set of one or more memory devices 720 in some embodiments. Alternatively, in other embodiments, each processor 820 may be communicatively coupled to each memory device 720. In some embodiments, the memory devices 720 may be mounted to one or more memory mezzanines on the bottom side of the chassis-less circuit board substrate 602 and may interconnect with a corresponding processor 820 through a ball-grid array.

Each of the processors 820 includes a heatsink 850 secured thereto. Due to the mounting of the memory devices 720 to the bottom side 750 of the chassis-less circuit board substrate 602 (as well as the vertical spacing of the sleds 400 in the corresponding rack 240), the top side 650 of the chassis-less circuit board substrate 602 includes additional “free” area or space that facilitates the use of heatsinks 850 having a larger size relative to traditional heatsinks used in typical servers. Additionally, due to the improved thermal cooling characteristics of the chassis-less circuit board substrate 602, none of the processor heatsinks 850 include cooling fans attached thereto. That is, each of the heatsinks 850 is embodied as a fan-less heatsink. In some embodiments, the heat sinks 850 mounted atop the processors 820 may overlap with the heat sink attached to the communication circuit 830 in the direction of the airflow path 608 due to their increased size, as illustratively suggested by FIG. 9.

Referring now to FIG. 10, in some embodiments, the sled 400 may be embodied as an accelerator sled 1000. The accelerator sled 1000 is configured, to perform specialized compute tasks, such as machine learning, encryption, hashing, or other computational-intensive task. In some embodiments, for example, a compute sled 800 may offload tasks to the accelerator sled 1000 during operation. The accelerator sled 1000 includes various components similar to components of the sled 400 and/or compute sled 800, which have been identified in FIG. 10 using the same reference numbers. The description of such components provided above in regard to FIGS. 6, 7, and 8 apply to the corresponding components of the accelerator sled 1000 and is not repeated herein for clarity of the description of the accelerator sled 1000.

In the illustrative accelerator sled 1000, the physical resources 620 are embodied as accelerator circuits 1020. Although only two accelerator circuits 1020 are shown in FIG. 10, it should be appreciated that the accelerator sled 1000 may include additional accelerator circuits 1020 in other embodiments. For example, as shown in FIG. 11, the accelerator sled 1000 may include four accelerator circuits 1020 in some embodiments. The accelerator circuits 1020 may be embodied as any type of processor, co-processor, compute circuit, or other device capable of performing compute or processing operations. For example, the accelerator circuits 1020 may be embodied as, for example, field programmable gate arrays (FPGA), application-specific integrated circuits (ASICs), security co-processors, graphics processing units (GPUs), neuromorphic processor units, quantum computers, machine learning circuits, or other specialized processors, controllers, devices, and/or circuits.

In some embodiments, the accelerator sled 1000 may also include an accelerator-to-accelerator interconnect 1042. Similar to the resource-to-resource interconnect 624 of the sled 600 discussed above, the accelerator-to-accelerator interconnect 1042 may be embodied as any type of communication interconnect capable of facilitating accelerator-to-accelerator communications. In the illustrative embodiment, the accelerator-to-accelerator interconnect 1042 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the accelerator-to-accelerator interconnect 1042 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications. In some embodiments, the accelerator circuits 1020 may be daisy-chained with a primary accelerator circuit 1020 connected to the NIC 832 and memory 720 through the I/O subsystem 622 and a secondary accelerator circuit 1020 connected to the NIC 832 and memory 720 through a primary accelerator circuit 1020.

Referring now to FIG. 11, an illustrative embodiment of the accelerator sled 1000 is shown. As discussed above, the accelerator circuits 1020, communication circuit 830, and optical data connector 834 are mounted to the top side 650 of the chassis-less circuit board substrate 602. Again, the individual accelerator circuits 1020 and communication circuit 830 are mounted to the top side 650 of the chassis-less circuit board substrate 602 such that no two heat-producing, electrical components shadow each other as discussed above. The memory devices 720 of the accelerator sled 1000 are mounted to the bottom side 750 of the of the chassis-less circuit board substrate 602 as discussed above in regard to the sled 600. Although mounted to the bottom side 750, the memory devices 720 are communicatively coupled to the accelerator circuits 1020 located on the top side 650 via the I/O subsystem 622 (e.g., through vias). Further, each of the accelerator circuits 1020 may include a heatsink 1070 that is larger than a traditional heatsink used in a server. As discussed above with reference to the heatsinks 870, the heatsinks 1070 may be larger than traditional heatsinks because of the “free” area provided by the memory resources 720 being located on the bottom side 750 of the chassis-less circuit board substrate 602 rather than on the top side 650.

Referring now to FIG. 12, in some embodiments, the sled 400 may be embodied as a storage sled 1200. The storage sled 1200 is configured, to store data in a data storage 1250 local to the storage sled 1200. For example, during operation, a compute sled 800 or an accelerator sled 1000 may store and retrieve data from the data storage 1250 of the storage sled 1200. The storage sled 1200 includes various components similar to components of the sled 400 and/or the compute sled 800, which have been identified in FIG. 12 using the same reference numbers. The description of such components provided above in regard to FIGS. 6, 7, and 8 apply to the corresponding components of the storage sled 1200 and is not repeated herein for clarity of the description of the storage sled 1200.

In the illustrative storage sled 1200, the physical resources 620 are embodied as storage controllers 1220. Although only two storage controllers 1220 are shown in FIG. 12, it should be appreciated that the storage sled 1200 may include additional storage controllers 1220 in other embodiments. The storage controllers 1220 may be embodied as any type of processor, controller, or control circuit capable of controlling the storage and retrieval of data into the data storage 1250 based on requests received via the communication circuit 830. In the illustrative embodiment, the storage controllers 1220 are embodied as relatively low-power processors or controllers. For example, in some embodiments, the storage controllers 1220 may be configured to operate at a power rating of about 75 watts.

In some embodiments, the storage sled 1200 may also include a controller-to-controller interconnect 1242. Similar to the resource-to-resource interconnect 624 of the sled 400 discussed above, the controller-to-controller interconnect 1242 may be embodied as any type of communication interconnect capable of facilitating controller-to-controller communications. In the illustrative embodiment, the controller-to-controller interconnect 1242 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the controller-to-controller interconnect 1242 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications.

Referring now to FIG. 13, an illustrative embodiment of the storage sled 1200 is shown. In the illustrative embodiment, the data storage 1250 is embodied as, or otherwise includes, a storage cage 1252 configured to house one or more solid state drives (SSDs) 1254. To do so, the storage cage 1252 includes a number of mounting slots 1256, each of which is configured to receive a corresponding solid state drive 1254. Each of the mounting slots 1256 includes a number of drive guides 1258 that cooperate to define an access opening 1260 of the corresponding mounting slot 1256. The storage cage 1252 is secured to the chassis-less circuit board substrate 602 such that the access openings face away from (i.e., toward the front of) the chassis-less circuit board substrate 602. As such, solid state drives 1254 are accessible while the storage sled 1200 is mounted in a corresponding rack 204. For example, a solid state drive 1254 may be swapped out of a rack 240 (e.g., via a robot) while the storage sled 1200 remains mounted in the corresponding rack 240.

The storage cage 1252 illustratively includes sixteen mounting slots 1256 and is capable of mounting and storing sixteen solid state drives 1254. Of course, the storage cage 1252 may be configured to store additional or fewer solid state drives 1254 in other embodiments. Additionally, in the illustrative embodiment, the solid state drivers are mounted vertically in the storage cage 1252, but may be mounted in the storage cage 1252 in a different orientation in other embodiments. Each solid state drive 1254 may be embodied as any type of data storage device capable of storing long term data. To do so, the solid state drives 1254 may include volatile and non-volatile memory devices discussed above.

As shown in FIG. 13, the storage controllers 1220, the communication circuit 830, and the optical data connector 834 are illustratively mounted to the top side 650 of the chassis-less circuit board substrate 602. Again, as discussed above, any suitable attachment or mounting technology may be used to mount the electrical components of the storage sled 1200 to the chassis-less circuit board substrate 602 including, for example, sockets (e.g., a processor socket), holders, brackets, soldered connections, and/or other mounting or securing techniques.

As discussed above, the individual storage controllers 1220 and the communication circuit 830 are mounted to the top side 650 of the chassis-less circuit board substrate 602 such that no two heat-producing, electrical components shadow each other. For example, the storage controllers 1220 and the communication circuit 830 are mounted in corresponding locations on the top side 650 of the chassis-less circuit board substrate 602 such that no two of those electrical components are linearly in-line with each other along the direction of the airflow path 608.

The memory devices 720 of the storage sled 1200 are mounted to the bottom side 750 of the of the chassis-less circuit board substrate 602 as discussed above in regard to the sled 400. Although mounted to the bottom side 750, the memory devices 720 are communicatively coupled to the storage controllers 1220 located on the top side 650 via the I/O subsystem 622. Again, because the chassis-less circuit board substrate 602 is embodied as a double-sided circuit board, the memory devices 720 and the storage controllers 1220 may be communicatively coupled by one or more vias, connectors, or other mechanisms extending through the chassis-less circuit board substrate 602. Each of the storage controllers 1220 includes a heatsink 1270 secured thereto. As discussed above, due to the improved thermal cooling characteristics of the chassis-less circuit board substrate 602 of the storage sled 1200, none of the heatsinks 1270 include cooling fans attached thereto. That is, each of the heatsinks 1270 is embodied as a fan-less heatsink.

Referring now to FIG. 14, in some embodiments, the sled 400 may be embodied as a memory sled 1400. The storage sled 1400 is optimized, or otherwise configured, to provide other sleds 400 (e.g., compute sleds 800, accelerator sleds 1000, etc.) with access to a pool of memory (e.g., in two or more sets 1430, 1432 of memory devices 720) local to the memory sled 1200. For example, during operation, a compute sled 800 or an accelerator sled 1000 may remotely write to and/or read from one or more of the memory sets 1430, 1432 of the memory sled 1200 using a logical address space that maps to physical addresses in the memory sets 1430, 1432. The memory sled 1400 includes various components similar to components of the sled 400 and/or the compute sled 800, which have been identified in FIG. 14 using the same reference numbers. The description of such components provided above in regard to FIGS. 6, 7, and 8 apply to the corresponding components of the memory sled 1400 and is not repeated herein for clarity of the description of the memory sled 1400.

In the illustrative memory sled 1400, the physical resources 620 are embodied as memory controllers 1420. Although only two memory controllers 1420 are shown in FIG. 14, it should be appreciated that the memory sled 1400 may include additional memory controllers 1420 in other embodiments. The memory controllers 1420 may be embodied as any type of processor, controller, or control circuit capable of controlling the writing and reading of data into the memory sets 1430, 1432 based on requests received via the communication circuit 830. In the illustrative embodiment, each memory controller 1420 is connected to a corresponding memory set 1430, 1432 to write to and read from memory devices 720 within the corresponding memory set 1430, 1432 and enforce any permissions (e.g., read, write, etc.) associated with sled 400 that has sent a request to the memory sled 1400 to perform a memory access operation (e.g., read or write).

In some embodiments, the memory sled 1400 may also include a controller-to-controller interconnect 1442. Similar to the resource-to-resource interconnect 624 of the sled 400 discussed above, the controller-to-controller interconnect 1442 may be embodied as any type of communication interconnect capable of facilitating controller-to-controller communications. In the illustrative embodiment, the controller-to-controller interconnect 1442 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the controller-to-controller interconnect 1442 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications. As such, in some embodiments, a memory controller 1420 may access, through the controller-to-controller interconnect 1442, memory that is within the memory set 1432 associated with another memory controller 1420. In some embodiments, a scalable memory controller is made of multiple smaller memory controllers, referred to herein as “chiplets”, on a memory sled (e.g., the memory sled 1400). The chiplets may be interconnected (e.g., using EMIB (Embedded Multi-Die Interconnect Bridge)). The combined chiplet memory controller may scale up to a relatively large number of memory controllers and I/O ports, (e.g., up to 16 memory channels). In some embodiments, the memory controllers 1420 may implement a memory interleave (e.g., one memory address is mapped to the memory set 1430, the next memory address is mapped to the memory set 1432, and the third address is mapped to the memory set 1430, etc.). The interleaving may be managed within the memory controllers 1420, or from CPU sockets (e.g., of the compute sled 800) across network links to the memory sets 1430, 1432, and may improve the latency associated with performing memory access operations as compared to accessing contiguous memory addresses from the same memory device.

Further, in some embodiments, the memory sled 1400 may be connected to one or more other sleds 400 (e.g., in the same rack 240 or an adjacent rack 240) through a waveguide, using the waveguide connector 1480. In the illustrative embodiment, the waveguides are 64 millimeter waveguides that provide 16 Rx (i.e., receive) lanes and 16 Tx (i.e., transmit) lanes. Each lane, in the illustrative embodiment, is either 16 GHz or 32 GHz. In other embodiments, the frequencies may be different. Using a waveguide may provide high throughput access to the memory pool (e.g., the memory sets 1430, 1432) to another sled (e.g., a sled 400 in the same rack 240 or an adjacent rack 240 as the memory sled 1400) without adding to the load on the optical data connector 834.

Referring now to FIG. 15, a system for executing one or more workloads (e.g., applications) may be implemented in accordance with the data center 100. In the illustrative embodiment, the system 1510 includes an orchestrator server 1520, which may be embodied as a managed node comprising a compute device (e.g., a processor 820 on a compute sled 800) executing management software (e.g., a cloud operating environment, such as OpenStack) that is communicatively coupled to multiple sleds 400 including a large number of compute sleds 1530 (e.g., each similar to the compute sled 800), memory sleds 1540 (e.g., each similar to the memory sled 1400), accelerator sleds 1550 (e.g., each similar to the accelerator sled 1000), and storage sleds 1560 (e.g., each similar to the storage sled 1200). One or more of the sleds 1530, 1540, 1550, 1560 may be grouped into a managed node 1570, such as by the orchestrator server 1520, to collectively perform a workload (e.g., an application 1532 executed in a virtual machine or in a container). The managed node 1570 may be embodied as an assembly of physical resources 620, such as processors 820, memory resources 720, accelerator circuits 1020, or data storage 1250, from the same or different sleds 400. Further, the managed node may be established, defined, or “spun up” by the orchestrator server 1520 at the time a workload is to be assigned to the managed node or at any other time, and may exist regardless of whether any workloads are presently assigned to the managed node. In the illustrative embodiment, the orchestrator server 1520 may selectively allocate and/or deallocate physical resources 620 from the sleds 400 and/or add or remove one or more sleds 400 from the managed node 1570 as a function of quality of service (QoS) targets (e.g., performance targets associated with a throughput, latency, instructions per second, etc.) associated with a service level agreement for the workload (e.g., the application 1532). In doing so, the orchestrator server 1520 may receive telemetry data indicative of performance conditions (e.g., throughput, latency, instructions per second, etc.) in each sled 400 of the managed node 1570 and compare the telemetry data to the quality of service targets to determine whether the quality of service targets are being satisfied. The orchestrator server 1520 may additionally determine whether one or more physical resources may be deallocated from the managed node 1570 while still satisfying the QoS targets, thereby freeing up those physical resources for use in another managed node (e.g., to execute a different workload). Alternatively, if the QoS targets are not presently satisfied, the orchestrator server 1520 may determine to dynamically allocate additional physical resources to assist in the execution of the workload (e.g., the application 1532) while the workload is executing. Similarly, the orchestrator server 1520 may determine to dynamically deallocate physical resources from a managed node if the orchestrator server 1520 determines that deallocating the physical resource would result in QoS targets still being met.

Additionally, in some embodiments, the orchestrator server 1520 may identify trends in the resource utilization of the workload (e.g., the application 1532), such as by identifying phases of execution (e.g., time periods in which different operations, each having different resource utilizations characteristics, are performed) of the workload (e.g., the application 1532) and pre-emptively identifying available resources in the data center 100 and allocating them to the managed node 1570 (e.g., within a predefined time period of the associated phase beginning). In some embodiments, the orchestrator server 1520 may model performance based on various latencies and a distribution scheme to place workloads among compute sleds and other resources (e.g., accelerator sleds, memory sleds, storage sleds) in the data center 100. For example, the orchestrator server 1520 may utilize a model that accounts for the performance of resources on the sleds 400 (e.g., FPGA performance, memory access latency, etc.) and the performance (e.g., congestion, latency, bandwidth) of the path through the network to the resource (e.g., FPGA). As such, the orchestrator server 1520 may determine which resource(s) should be used with which workloads based on the total latency associated with each potential resource available in the data center 100 (e.g., the latency associated with the performance of the resource itself in addition to the latency associated with the path through the network between the compute sled executing the workload and the sled 400 on which the resource is located).

In some embodiments, the orchestrator server 1520 may generate a map of heat generation in the data center 100 using telemetry data (e.g., temperatures, fan speeds, etc.) reported from the sleds 400 and allocate resources to managed nodes as a function of the map of heat generation and predicted heat generation associated with different workloads, to maintain a target temperature and heat distribution in the data center 100. Additionally or alternatively, in some embodiments, the orchestrator server 1520 may organize received telemetry data into a hierarchical model that is indicative of a relationship between the managed nodes (e.g., a spatial relationship such as the physical locations of the resources of the managed nodes within the data center 100 and/or a functional relationship, such as groupings of the managed nodes by the customers the managed nodes provide services for, the types of functions typically performed by the managed nodes, managed nodes that typically share or exchange workloads among each other, etc.). Based on differences in the physical locations and resources in the managed nodes, a given workload may exhibit different resource utilizations (e.g., cause a different internal temperature, use a different percentage of processor or memory capacity) across the resources of different managed nodes. The orchestrator server 1520 may determine the differences based on the telemetry data stored in the hierarchical model and factor the differences into a prediction of future resource utilization of a workload if the workload is reassigned from one managed node to another managed node, to accurately balance resource utilization in the data center 100.

To reduce the computational load on the orchestrator server 1520 and the data transfer load on the network, in some embodiments, the orchestrator server 1520 may send self-test information to the sleds 400 to enable each sled 400 to locally (e.g., on the sled 400) determine whether telemetry data generated by the sled 400 satisfies one or more conditions (e.g., an available capacity that satisfies a predefined threshold, a temperature that satisfies a predefined threshold, etc.). Each sled 400 may then report back a simplified result (e.g., yes or no) to the orchestrator server 1520, which the orchestrator server 1520 may utilize in determining the allocation of resources to managed nodes.

Referring now to FIG. 16, a system 1610 for enabling and metering (e.g., tracking) the utilization of features of components on an as-requested (e.g., on demand) basis includes an orchestrator server 1620, similar to the orchestrator server 1520, in communication with multiple sleds 1616, including a compute sled 1622, similar to the compute sled 800, that executes workloads 1630, 1632 (e.g., sets of operations, such as software applications) similar to the application 1532, in corresponding virtual machines 1640, 1642 or containers, on behalf of a client device 1614. The illustrative sleds 1616 also include an accelerator sled 1624 similar to the accelerator sled 1000, a memory sled 1626 similar to the memory sled 1400, and a data storage sled 1628, similar to the data storage sled 1200. The orchestrator server 1620, in the illustrative embodiment, also includes a utilization manager logic unit 1697, which may be embodied as any device or circuitry (e.g., a processor, a controller, reconfigurable circuitry, an FPGA, an ASIC, etc.) or software configured to selectively enable or disable components, referred to herein as meterable components, of the sleds 1616 on an as-requested basis (e.g., in response to a request from a workload 1630, 1632) and meter (e.g., track) the utilization of those meterable components to determine corresponding costs to be passed on to the customer associated with the requesting workload 1630, 1632. As described above, for typical data centers, a service level agreement (SLA) may specify a set of quality of service (QoS) targets and a fee for satisfying the QoS targets, without regard to the specific hardware components utilized to satisfy the QoS targets. Accordingly, in the illustrative embodiment, metering by the orchestrator server 1620 may meter utilization outside of the SLA of the customer. Furthermore, in the illustrative embodiment, the orchestrator server 1620 includes a key manager logic unit 1698 which may be embodied as any device or circuitry (e.g., a processor, a controller, reconfigurable circuitry, an FPGA, an ASIC, etc.) or software configured to provide keys (e.g., license keys) to the sleds (e.g., to a workload that is requesting use of a meterable component) and to the meterable component that is to be utilized by the workload, to ensure that the meterable component only performs operations on behalf of workloads that have been authorized (e.g., by the orchestrator server 1620) to have operations performed by the corresponding meterable component. In some embodiments, the system 1610 may also include a provisioner compute device 1618, which may be embodied as any computer (e.g., a compute device having an architecture similar to the orchestrator server 1620) associated with a manufacturer of the meterable components of the sleds 1616 that communicates with the orchestrator server 1620 (e.g., receives requests from the orchestrator server 1620 to enable or disable certain meterable components or features thereof) and communicates with the corresponding sleds 1616 (e.g., with controllers of those sleds 1616) to selectively enable meterable components and features thereof in response to the requests from the orchestrator server 1620, as described in more detail herein.

In the illustrative embodiment, the compute sled 1622 includes an I/O (input and output) virtualization logic unit 1660, which may be embodied as any device or circuitry (e.g., an integrated circuit, a processor, etc.) configured to enable the isolation of I/O devices for use by virtual machines and containers, such as in single-root input/output virtualization (SR-IOV) or scalable I/O virtualization. As such, the I/O virtualization logic unit 1664 may individually assign any meterable component 1650, 1652, 1654, 1656 of any of the sleds 1616 to a corresponding workload (e.g., to a virtual machine or container in which the workload is executed). The meterable components 1650, in the illustrative embodiment, include a set of cores 1660, 1662, each of which may be embodied as a device or circuitry capable of reading and executing instructions to perform operations. In some embodiments, the I/O virtualization logic unit 1664 may be included in the set of meterable components 1650. Further, each meterable component 1650 (e.g., each core 1660, 1662) includes a corresponding controller 1664, 1666 each of which may be embodied as any device or circuitry (e.g., an integrated circuit, a processor, etc.) configured to selectively enable (e.g., provide power to) or disable (e.g., discontinue power to) the corresponding meterable component 1650, selectively enable, disable, or adjust certain features of the corresponding meterable component (e.g., an amount of memory capacity supported by the corresponding core, a memory bandwidth supported by the core, a cryptographic processing instruction set of the core, etc.), track the utilization of the meterable component (e.g., with a monotonic timer), store a key usable for encryption and to verify permissions on requests (e.g., from workloads), report features of the meterable component including the enabled features and the disabled features, report usage of each enabled meterable component (e.g., how long a particular workload has utilized the feature(s), how many operations have been performed using the feature for the workload, etc.) and report a unique identifier of the corresponding meterable component 1650 to another compute device (e.g., to the orchestrator server or to an intermediary compute device that may aggregate data reported by multiple controllers of meterable components and send the aggregated data to the orchestrator server 1620).

In the accelerator sled 1624, the meterable components 1652 include a set of accelerator devices 1670, 1672, similar to the accelerator circuits 1020 of the accelerator device 1020, and may include FPGAs, ASICs, neural network processor units (NNPUs), graphics processing units (GPUs), quantum computers, neuromorphic processor units, or other devices capable of performing operations faster than a general purpose processor. The meterable components 1652 each include controllers 1674, 1676 which are similar to the controllers 1664, 1666 described above with reference to the meterable components 1650 of the compute sled 1622. The features of the meterable components 1652 may include a number of slots (e.g., sets of gates of a field programmable gate array (FPGA) available to be configured with a bit stream (e.g., a set of data defining a configuration of the gates to implement a particular function)) available, bit streams available for use, a memory capacity and/or memory bandwidth supported by each meterable component 1652, or other features. The meterable components 1654 of the memory sled 1626 include multiple memory devices 1680, 1682 similar to the memory devices 720. The meterable components 1654 each include controllers 1684, 1686 similar to the controllers 1664, 1666 described above. The features of the meterable components 1654 may include a memory bandwidth supported, memory capacity supported, a type of memory architecture utilized (e.g., dynamic random access memory, 3DXP, etc.), and/or other features. The meterable components 1656 of the data storage sled 1628 include multiple data storage devices 1690, 1692, similar to the data storage 1250 (e.g., solid state drives 1254, hard disk drives, etc.). The meterable components 1656 each include controllers 1694, 1696 similar to the controllers 1664, 1666 described above. The features of the meterable components 1656 may include the storage capacity, read/write throughput (e.g., bytes per second, etc.), and/or other features. By selectively enabling the hardware components (e.g., the meterable components 1650, 1652, 1654, 1656) and specific features of those components for workloads on an as-requested basis, the system 1610 provides more fine grained control and tracking of costs incurred during the execution of services (e.g., workloads) and enables more cost efficient operation (e.g., in terms of power usage and monetary cost) of the data center, as compared to typical data centers. Furthermore, by providing meterable components with sets of available features that may be selectively enabled and disabled, on an as requested basis, the administrator of the system 1610 may need to only track one SKU (stock keeping unit) for each type of meterable component (core, accelerator device, data storage device, memory device, etc.), rather than multiple SKUs for different variations on each type of meterable component (e.g., one version of a data storage device has features A, B, and C, while another version of the data storage device only has features A and B).

The orchestrator server 1620, the sleds 1616, the provisioner compute device 1618, and the client device 1614 are illustratively in communication via a network 1612, which may be embodied as any type of wired or wireless communication network, including global networks (e.g., the Internet), local area networks (LANs) or wide area networks (WANs), cellular networks (e.g., Global System for Mobile Communications (GSM), 3G, Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), etc.), digital subscriber line (DSL) networks, cable networks (e.g., coaxial networks, fiber networks, etc.), or any combination thereof.

Referring now to FIG. 17, in operation, the orchestrator server 1620 may execute a method 1700 for enabling and metering the utilization of components (e.g., the meterable components 1650, 1652, 1654, 1656) on demand. The method 1700 begins with block 1702 in which the orchestrator server 1620 determines whether to enable component and feature utilization metering. In doing so, the orchestrator server 1620 may determine to enable metering in response to detecting a set of available components and the features of those components (e.g., the features of the meterable components 1650, 1652, 1654, 1656) as reported by the corresponding controllers 1664, 1666, 1674, 1676, 1684, 1686, 1694, 1696 (e.g., in response to a query from the orchestrator server 1620), detecting that a setting in a configuration file accessible to the orchestrator server (e.g., configured by an administrator of the system 1610) indicates to enable component and feature utilization metering, in response to a determination that the orchestrator server 1620 is equipped with the utilization manager logic unit 1697, and/or based on other factors. Regardless, in response to a determination to enable component utilization metering, the method 1700 advances to block 1704 in which the orchestrator server 1620 may disable all of the meterable components 1650, 1652, 1654, 1656 and their features (e.g., as a default state, such that they may be selectively enabled upon request, as described in more detail herein). In doing so, the orchestrator server 1620 may send a request to the sleds 1616 in the data center to disable each meterable component 1650, 1652, 1654, 1656, as indicated in block 1706. Further, the orchestrator server 1620 may request the sleds 1616 to not provide power to the meterable components 1650, 1652, 1654, 1556 (e.g., to conserve electricity), as indicated in block 1708. Accordingly, each corresponding controller 1664, 1666, 1674, 1676, 1684, 1686, 1694, 1696, in the illustrative embodiment, may discontinue any provisioning of power to the corresponding meterable components.

Subsequently, in block 1710, the orchestrator server 1620, in the illustrative embodiment, receives a request to enable one or more components, and optionally, certain features of those components, in the data center (e.g., in the system 1610) to assist in the execution of a workload. In doing so, in the illustrative embodiment, the orchestrator server 1620 receives the request from a compute sled (e.g., the compute sled 1622) assigned to execute the workload (e.g., the workload 1630), as indicated in block 1712. As indicated in block 1714, in the illustrative embodiment, the orchestrator server 1620 receives a request that identifies the type(s) of component(s) to enable. For example, and as indicated in block 1716, the orchestrator server 1620 may receive a request that identifies a requested feature set (e.g., a set of operations that the component(s) should be able to perform). The request may indicate the feature set using any data (e.g., numbers or codes that are associated with each type of service, a textual description of the features, which may be matched, by the orchestrator server 1620 against a database of features of each meterable component 1650, 1652, 1654, 1656 of each sled 1616, etc.) indicative of one or more features. In doing so, the orchestrator server 1620 may receive a request that identifies I/O virtualization functions, similar to those provided by the I/O virtualization logic unit 1664 described with reference to FIG. 16, as indicated in block 1718. Additionally or alternatively, the orchestrator server 1620 may receive a request that identifies acceleration operations, corresponding to features of one or more of the accelerator devices 1670, 1672, as indicated in block 1720. For example, the orchestrator server 1620 may receive a request that identifies machine learning operations, as indicated in block 1722. Additionally or alternatively, the request may identify cryptographic operations (e.g., encryption and/or decryption of data) to be accelerated, as indicated in block 1724 or may identify data compression and/or decompression operations, as indicated in block 1726. The orchestrator server 1620 may additionally or alternatively receive a request that identifies memory operations (e.g., a request to have read and write access to a specified amount of volatile memory), as indicated in block 1728. Similarly, the orchestrator server 1620 may receive a request that identifies data storage operations (e.g., a request to have access to block storage or object storage), as indicated in block 1730. The requested features may also include a supported memory bandwidth of the enabled component, an instruction set to be supported by a component (e.g., by a core 1660), a number of FPGA slots to enable, or other features. Further, the request may include the quantity of each type of component to enable (e.g., number of data storage devices, a number of cores, etc.). As indicated in block 1732, the orchestrator server 1620 may receive a request that additionally indicates a performance target to be satisfied by the component(s) (e.g., a target number of I/O operations per second, a target number of neural network convolution operations per second, a target number of floating point operations per second, a target latency, etc.). The orchestrator server 1620 may also receive a request that includes data indicative of the total amount of utilization being requested, as indicated in block 1734. For example, and as indicated in block 1736, the orchestrator server 1620 may receive a request that identifies a total cost (e.g., a monetary cost) that is not to be exceeded and/or a total time period in which the components are to be utilized by the workload 1630. The orchestrator server 1620 may also receive, in the request, data indicative of a cost per unit of time (e.g., per second) or per operation (e.g., per write or read of data, per encryption or de-encryption of a data set, etc.), as indicated in block 1738. Subsequently, the method 1700 advances to block 1740 of FIG. 18, in which the orchestrator server 1620 identifies components to enable.

Referring now to FIG. 18, in identifying the components to enable, the orchestrator server 1620, in the illustrative embodiment, identifies one or more components (e.g., of the meterable components 1650, 1652, 1654, 1656) that match the requested type(s) of components (e.g., by comparing the requested features to a database of features and performance associated with each meterable component 1650, 1652, 1654, 1656), as indicated in block 1742. As such, and as indicated in block 1744, the orchestrator server 1620 may identify component(s) configured to perform operations identified in the request, as indicated in block 1744, and may identify, of those components, the components that are configured to satisfy the requested performance targets, as indicated in block 1746. Further, and as indicated in block 1748, the orchestrator server 1620 may further narrow the selection by identifying component(s) (e.g., one or more of the meterable components 1650, 1652, 1654, 1656) that also satisfy the requested cost per unit of time or cost per operation (e.g., by comparing the costs specified in block 1738 of FIG. 17 to a database of costs associated with the features of the meterable components 1650, 1652, 1654, 1656), as indicated in block 1748.

Subsequently, and as indicated in block 1750, the orchestrator server 1620 enables the identified component(s). In doing so, and as indicated in block 1752, the orchestrator server 1620 may enable one or more components of a compute sled (e.g., one or more of the meterable components 1650 of the compute sled 1622). In doing so, in the illustrative embodiment, the orchestrator server 1620 enables the I/O virtualization logic unit 1664, as indicated in block 1754. The orchestrator server 1620 may also enable one or more of the cores 1662, as indicated in block 1756. As indicated in block 1758, the orchestrator server 1620 may enable one or more accelerator devices (e.g., one or more of the accelerator devices 1670, 1672 of the accelerator sled 1624). In doing so, the orchestrator server 1620 may enable an FPGA, as indicated in block 1760, a neural network processing unit (NNPU) as indicated in block 1762, a graphics processing unit (GPU) as indicated in block 1764, an ASIC as indicated in block 1766 and/or other meterable components 1654 of the accelerator sled 1626. Additionally or alternatively, the orchestrator server 1620 may enable one or more memory devices (e.g., one or more of the memory devices 1680, 1682 of the memory sled 1626), as indicated in block 1768 and/or one or more data storage devices (e.g., one or more of the data storage devices 1690, 1692 of the data storage sled 1628), as indicated in block 1770. In some embodiments, the orchestrator server 1620 may send a request to the provisioner compute device 1618, identifying the meterable component and feature(s) thereof to be enabled (e.g., by a specified unique identifier such as a universally unique identifier (UUID)), and the provisioner compute device 1618 sends a corresponding encrypted message to the corresponding sled 1616 to enable the component and feature(s) thereof. In some embodiments, as indicated in block 1772, to enable the component(s) and feature(s) of the component, the orchestrator server 1620 may send a request to the sled 1616 on which each identified component (e.g., each meterable component that is be enabled) is located to enable (e.g., provide power to) the component and specified features of that component. The request may include the unique identifier of the meterable component (usable by the receiving sled to identify and enable the corresponding component and features thereof, such as by providing a corresponding request to the corresponding controller 1664, 1666, 1674, 1676, 1684, 1686, 1694, 1696). The orchestrator server 1620 may also send the unique identifier of each component (e.g., a universally unique identifier (UUID), an address, such as a media access control (MAC) address or internet protocol (IP) of the component, etc.) to the workload (e.g., the workload 1630) to enable the workload to send requests to the component(s), as indicated in block 1774. Further, in the illustrative embodiment, the orchestrator server 1620 sends a license key (e.g., a cryptographic key) associated with each enabled component to the workload to enable the workload 1630 to use the component and the selected features thereof (e.g., by including the license key in any requests to the corresponding component to perform one or more operations). The orchestrator server 1620 may also send those license keys to the corresponding components for use in verifying (e.g., comparing the license key in a request from the workload to the license key received from the orchestrator server 1620) requests from the workload. In other embodiments, the components may already have the license keys (e.g., stored in firmware of each component) and/or the license key may be provided by the provisioner compute device 1618. As indicated in block 1778, the orchestrator server 1620 may also send, to each enabled component, an identifier of the workload that has been given authorization to utilize those components and features thereof (e.g., for use in verifying requests from the workload). Subsequently, the method 1700 advances to block 1780 of FIG. 19 in which the orchestrator server 1620 meters the utilization of the enabled component(s) and features thereof.

Referring now to FIG. 19, in metering the utilization of the enabled component(s) and features thereof, the orchestrator server 1620 may monitor the amount of time that each component and features thereof have been utilized (e.g., monitor the amount of time since the components were enabled for the workload, in block 1750 of FIG. 18), as indicated in block 1782. Information indicative of the amount of time that each component and features thereof have been utilized may be reported by each corresponding controller 1664, 1666, 1674, 1676, 1684, 1686, 1694, 1696 to the orchestrator server 1620 or to the provisioner compute device 1618, which may then report the information to the orchestrator server 1620. In some embodiments, the information may be aggregated by one or more compute devices (e.g., a pod controller, a rack controller, etc.) before being sent to the orchestrator server 1620 and/or to the provisioner compute device 1618. As indicated in block 1784, the orchestrator server 1620 may monitor the number of operations that each component has performed for the workload (e.g., by receiving telemetry data from the corresponding sleds indicative of the operations performed for the workload). Further, and as indicated in block 1786, the orchestrator server 1620 may determine a total cost for the utilization of the enabled component(s) (e.g., by multiplying the amount of time from block 1782 by a cost per unit of time or by multiplying the number of operations by a cost per operation). In some embodiments, the orchestrator server 1620 may determine the total cost as a function of the amount of capacity (e.g., bandwidth) reserved for a workload over a particular time period, regardless of whether the workload actually utilized that entire reserved capacity over the time period. In some embodiments, the orchestrator server 1620 may multiply the determined total cost by a factor (e.g., 1.10) to increase the total cost if the requested components and features were utilized during a period of high demand (e.g., a time period in which the orchestrator server 1620 received at least a predefined number of requests for the component/feature). In other embodiments, the determination of the total cost is performed by the provisioner compute device 1618 and reported to the orchestrator server 1620.

Subsequently, in block 1788, the orchestrator server 1620 may determine whether to discontinue the utilization of the component(s). In doing so, and as indicated in block 1790, the orchestrator server 1620 may determine whether the total cost for utilization of the component(s) (e.g., from block 1786) satisfies (e.g., is equal to) a threshold cost, such as the cost specified in block 1736. If so, the orchestrator server 1620, in the illustrative embodiment, determines to discontinue utilization of the component(s). Additionally or alternatively, the orchestrator server 1620 may receive a request from the workload 1630 to discontinue utilization of the component(s), as indicated in block 1792. In block 1794, the orchestrator server 1620 determines the subsequent course of action as a function of the determination made in block 1788 (e.g., whether to discontinue utilization of the component(s)). If not, the method 1700 loops back to block 1710 of FIG. 17, in which the orchestrator server 1620 may receive and respond to another request from a workload (e.g., the workload 1630 or the workload 1632) to enable one or more components. Otherwise, the method 1700 advances to block 1796 in which the orchestrator server 1620 disables the component(s).

In disabling the component(s), the orchestrator server 1620 may send a request to each sled 1616 on which each enabled component is located to disable the corresponding component, as indicated in block 1798. As indicated in block 1798, in doing so, the orchestrator server 1620 may send a request to each corresponding sled 1616 to stop providing power to the corresponding component, as indicated in block 1800. Further, the orchestrator server 1620 may send a notification to the workload (e.g., the workload 1630) that the one or more components have been disabled, as indicated in block 1802. As indicated in block 1804, the orchestrator server 1620 may also send a replacement license key to each component being disabled, for use in verifying request(s) from a workload in the future (e.g., expiring the license key from block 1776). The orchestrator server 1620, in the illustrative embodiment, also discontinues metering of the disabled components, as indicated in block 1806 and, as indicated in block 1808, may bill (e.g., deduct money from an account, send a request for payment, etc.) the customer associated with the workload (e.g., the workload 1630) for utilization of the component(s). In some embodiments, the request to enable a component and potentially specific features of the component may include the amount of time that the component and feature(s) is to be enabled and the components/features are deactivated upon expiration of that time period (e.g., by the corresponding controller 1664, 1666, 1674, 1676, 1684, 1686, 1694, 1696) without any affirmative action on the part of the orchestrator server 1620. Further, in some embodiments, one or more of the operations of blocks 1780 through 1808 may be performed by the provisioner compute device 1618, rather than the orchestrator server 1620. Additionally, in some embodiments, the provisioner compute device 1618 may provide an invoice to the operator of the system 1610 (e.g., to the orchestrator server 1620) for the utilization of the components, and the orchestrator server 1620 may subsequently pass on all or a portion of the cost to customer(s) associated with the workloads that utilized the components. Subsequently, the method 1700 loops back to block 1710 of FIG. 17, in which the orchestrator server 1620 may receive another request to enable one or more components.

EXAMPLES

Illustrative examples of the technologies disclosed herein are provided below. An embodiment of the technologies may include any one or more, and any combination of, the examples described below.

Example 1 includes a compute device comprising a network interface controller; and circuitry to receive, through a network and with the network interface controller, a request to enable a component of a sled to assist in the execution of a workload; enable, in response to the request, the component to assist in the execution of the workload; and meter the utilization of the component by the workload to determine a total monetary cost to a customer associated with the workload for the utilization of the component.

Example 2 includes the subject matter of Example 1, and wherein the circuitry is further to send, to a compute sled assigned to execute the workload, a license key to include in one or more requests to the component to execute one or more operations.

Example 3 includes the subject matter of any of Examples 1 and 2, and wherein to enable the component comprises to send a request to the sled on which the component is located to provide power to the component.

Example 4 includes the subject matter of any of Examples 1-3, and wherein to receive a request to enable a component comprises to receive a request that includes data indicative of a type of component to enable and the circuity is further to identify, as a function of data included in the request, the component to enable.

Example 5 includes the subject matter of any of Examples 1-4, and wherein to receive a request to enable a component comprises to receive a request that includes utilization limit data indicative of a total amount of utilization requested, and wherein the circuitry is further to determine whether a present total cost of utilization of the component satisfies the utilization limit data; and disable, in response to a determination that the present total cost of utilization satisfies the utilization limit data, the component.

Example 6 includes the subject matter of any of Examples 1-5, and wherein the circuitry is further to receive a request from the workload to discontinue utilization of the component; and send, in response to the request to discontinue utilization, a request to the sled on which the component is located to no longer provide power to the component.

Example 7 includes the subject matter of any of Examples 1-6, and wherein the circuitry is further to determine whether to discontinue utilization, by the workload, of the component; and send, in response to a determination to discontinue utilization, a request to the sled on which the component is located to disable the component.

Example 8 includes the subject matter of any of Examples 1-7, and wherein the circuitry is further to send, to the sled on which the component is located, a replacement license key for use in verifying subsequent requests by a workload to perform one or more operations with the component.

Example 9 includes the subject matter of any of Examples 1-8, and wherein to enable the component comprises to enable an I/O virtualization logic unit.

Example 10 includes the subject matter of any of Examples 1-9, and wherein to enable the component comprises to enable a core of the compute sled.

Example 11 includes the subject matter of any of Examples 1-10, and wherein to enable the component comprises to enable an accelerator device.

Example 12 includes the subject matter of any of Examples 1-11, and wherein to enable the component comprises to enable a memory device.

Example 13 includes the subject matter of any of Examples 1-12, and wherein to enable the component comprises to enable a data storage device.

Example 14 includes the subject matter of any of Examples 1-13, and wherein to enable the component comprises to send a request to a provisioner compute device to send a message to the sled to enable the component.

Example 15 includes the subject matter of any of Examples 1-14, and wherein to receive the request to enable a component comprises to receive a request to enable a specified feature of a set of features supported by the component; and wherein to enable the component comprises to enable the specified feature of the component.

Example 16 includes the subject matter of any of Examples 1-15, and wherein to meter the utilization of the component by the workload comprises to meter utilization of the component outside of a service-level agreement of the customer.

Example 17 includes one or more machine-readable storage media comprising a plurality of instructions stored thereon that, in response to being executed, cause a compute device to receive, through a network, a request to enable a component of a sled to assist in the execution of a workload; enable, in response to the request, the component to assist in the execution of the workload; and meter the utilization of the component by the workload to determine a total monetary cost to a customer associated with the workload for the utilization of the component.

Example 18 includes the subject matter of Example 17, and wherein the plurality of instructions further cause the compute device to send, to a compute sled assigned to execute the workload, a license key to include in one or more requests to the component to execute one or more operations.

Example 19 includes the subject matter of any of Examples 17 and 18, and wherein to enable the component comprises to send a request to the sled on which the component is located to provide power to the component.

Example 20 includes the subject matter of any of Examples 17-19, and wherein to receive a request to enable a component comprises to receive a request that includes data indicative of a type of component to enable and the plurality of instructions further cause the compute device to identify, as a function of data included in the request, the component to enable.

Example 21 includes the subject matter of any of Examples 17-20, and wherein to receive a request to enable a component comprises to receive a request that includes utilization limit data indicative of a total amount of utilization requested, and wherein the plurality of instructions further cause the compute device to determine whether a present total cost of utilization of the component satisfies the utilization limit data; and disable, in response to a determination that the present total cost of utilization satisfies the utilization limit data, the component.

Example 22 includes a method comprising receiving, by a compute device and through a network, a request to enable a component of a sled to assist in the execution of a workload; enabling, by the compute device and in response to the request, the component to assist in the execution of the workload; and metering, by the compute device, the utilization of the component by the workload to determine a total monetary cost to a customer associated with the workload for the utilization of the component.

Example 23 includes the subject matter of Example 22, and further including sending, by the compute device and to a compute sled assigned to execute the workload, a license key to include in one or more requests to the component to execute one or more operations.

Example 24 includes the subject matter of any of Examples 22 and 23, and wherein enabling the component comprises sending a request to the sled on which the component is located to provide power to the component.

Example 25 includes the subject matter of any of Examples 22-24, and wherein receiving a request to enable a component comprises receiving a request that includes data indicative of a type of component to enable, the method further comprising identifying, by the compute device and as a function of data included in the request, the component to enable. 

1-25. (canceled)
 26. A compute device comprising: a network interface controller; and circuitry to: receive, through a network and with the network interface controller, a request to enable a component of a sled to assist in the execution of a workload; enable, in response to the request, the component to assist in the execution of the workload; and meter the utilization of the component by the workload to determine a total monetary cost to a customer associated with the workload for the utilization of the component.
 27. The compute device of claim 26, wherein the circuitry is further to send, to a compute sled assigned to execute the workload, a license key to include in one or more requests to the component to execute one or more operations.
 28. The compute device of claim 26, wherein to enable the component comprises to send a request to the sled on which the component is located to provide power to the component.
 29. The compute device of claim 26, wherein to receive a request to enable a component comprises to receive a request that includes data indicative of a type of component to enable and the circuity is further to identify, as a function of data included in the request, the component to enable.
 30. The compute device of claim 26, wherein to receive a request to enable a component comprises to receive a request that includes utilization limit data indicative of a total amount of utilization requested, and wherein the circuitry is further to: determine whether a present total cost of utilization of the component satisfies the utilization limit data; and disable, in response to a determination that the present total cost of utilization satisfies the utilization limit data, the component.
 31. The compute device of claim 26, wherein the circuitry is further to: receive a request from the workload to discontinue utilization of the component; and send, in response to the request to discontinue utilization, a request to the sled on which the component is located to no longer provide power to the component.
 32. The compute device of claim 26, wherein the circuitry is further to: determine whether to discontinue utilization, by the workload, of the component; and send, in response to a determination to discontinue utilization, a request to the sled on which the component is located to disable the component.
 33. The compute device of claim 32, wherein the circuitry is further to send, to the sled on which the component is located, a replacement license key for use in verifying subsequent requests by a workload to perform one or more operations with the component.
 34. The compute device of claim 26, wherein to enable the component comprises to enable an I/O virtualization logic unit.
 35. The compute device of claim 26, wherein to enable the component comprises to enable a core of the compute sled.
 36. The compute device of claim 26, wherein to enable the component comprises to enable an accelerator device.
 37. The compute device of claim 26, wherein to enable the component comprises to enable a memory device.
 38. The compute device of claim 26, wherein to enable the component comprises to enable a data storage device.
 39. The compute device of claim 26, wherein to enable the component comprises to send a request to a provisioner compute device to send a message to the sled to enable the component.
 40. The compute device of claim 26, wherein to receive the request to enable a component comprises to receive a request to enable a specified feature of a set of features supported by the component; and wherein to enable the component comprises to enable the specified feature of the component.
 41. The compute device of claim 26, wherein to meter the utilization of the component by the workload comprises to meter utilization of the component outside of a service-level agreement of the customer.
 42. One or more machine-readable storage media comprising a plurality of instructions stored thereon that, in response to being executed, cause a compute device to: receive, through a network, a request to enable a component of a sled to assist in the execution of a workload; enable, in response to the request, the component to assist in the execution of the workload; and meter the utilization of the component by the workload to determine a total monetary cost to a customer associated with the workload for the utilization of the component.
 43. The one or more machine-readable storage media of claim 42, wherein the plurality of instructions further cause the compute device to send, to a compute sled assigned to execute the workload, a license key to include in one or more requests to the component to execute one or more operations.
 44. The one or more machine-readable storage media of claim 42, wherein to enable the component comprises to send a request to the sled on which the component is located to provide power to the component.
 45. The one or more machine-readable storage media of claim 42, wherein to receive a request to enable a component comprises to receive a request that includes data indicative of a type of component to enable and the plurality of instructions further cause the compute device to identify, as a function of data included in the request, the component to enable.
 46. The one or more machine-readable storage media of claim 42, wherein to receive a request to enable a component comprises to receive a request that includes utilization limit data indicative of a total amount of utilization requested, and wherein the plurality of instructions further cause the compute device to: determine whether a present total cost of utilization of the component satisfies the utilization limit data; and disable, in response to a determination that the present total cost of utilization satisfies the utilization limit data, the component.
 47. A method comprising: receiving, by a compute device and through a network, a request to enable a component of a sled to assist in the execution of a workload; enabling, by the compute device and in response to the request, the component to assist in the execution of the workload; and metering, by the compute device, the utilization of the component by the workload to determine a total monetary cost to a customer associated with the workload for the utilization of the component.
 48. The method of claim 47, further comprising sending, by the compute device and to a compute sled assigned to execute the workload, a license key to include in one or more requests to the component to execute one or more operations.
 49. The method of claim 47, wherein enabling the component comprises sending a request to the sled on which the component is located to provide power to the component.
 50. The method of claim 47, wherein receiving a request to enable a component comprises receiving a request that includes data indicative of a type of component to enable, the method further comprising identifying, by the compute device and as a function of data included in the request, the component to enable. 